The present disclosure teaches a camera-based system and method for estimating parking occupancy where the camera may be mounted on a mobile platform selectively deployable for temporary operation in a parking area for data collection. The disclosure contemplates use between multiple on-street parking environments, but is amendable to parking lots and other like environments.
One challenge that parking management companies face while managing parking operations is an accurate occupancy determination and future prediction capability about parking trends. Occupancy determination can be used, for example, for parking guidance, while occupancy prediction can be used to derive a dynamic pricing strategy for the managed parking area. This pricing strategy can require data on parking space usage patterns, which can also depend on the time of day, season, and/or scheduled events. To collect this occupancy data in a metered environment, some parking management companies use parking meter payment data as a surrogate of parking occupancy. The parking meter payment data can be insufficient because vehicles do not always park according to the exact time the meter is paid for, and some vehicles skip payment when they park at a meter that is still active after a previous vehicle departed.
The parking management company can alternatively monitor the parking spaces in the parking area. Existing methods for monitoring parking spaces and tracking vehicles occupying the spaces include sensor-based solutions. For example, “puck-style” sensors and ultrasonic ceiling or in-ground sensors output a binary signal when a vehicle is detected in the parking area. A disadvantage associated with these sensor-based methods is a high cost for installation and maintenance of the sensors. Therefore, camera monitoring systems were recently developed to detect and track vehicles by processing image frames acquired from a fixed video camera. Similar to the sensor-based solution, this technology was designed for permanent installation at the specific parking area being monitored. Therefore, an application of the occupancy data collected therefrom is limited to that specific parking area. Furthermore, a continuous collection of this data may not be necessary if the parking trends do not change over time. In this scenario, an installation of the monitoring system may not provide a substantial return on the investment.
A mobile parking occupancy estimation system and method is desired which is rapidly deployable for temporary operations between sites and is adapted to gather occupancy data from each specific site over a short period of time (e.g., a few days or week(s)). However, one foreseen challenge associated with a mobile system is that it would have to operate without receiving site-specific training for the specific parking area and/or configuration. One aspect of the existing camera monitoring system is that it typically acquires several days of video data to train a classifier used with the system. Unlike the existing stationary system, the mobile system cannot train a vehicle classifier using a constant background, as the background changes from site-to-site. The training can be considered necessary to maintain accuracy. However, where a system is desired to only temporarily collect data at the specific parking area, this site-specific training can be time consuming and exceed the duration that the mobile system is located at the site. In other words, the mobile system may not be provided the time necessary to ramp up to a suitable accuracy level for the specific parking area.
Because this disclosure anticipates a portable device that is moveable from site-to-site, each for a short period of time and possibly without returning, it becomes impractical for the site-specific training to work in this setting. Accordingly, a scalable system is desired which requires little to no site-specific training, re-training of classifiers, or parameter tuning, but one which still meets the desired accuracy levels. A selectively mobile system and method is desired which is operative to transform the different parking areas to a generally common view domain and train a classifier in the common view domain for improving accuracy.